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A new drought monitoring approach: Vector Projection Analysis (VPA)
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.rse.2020.112145
Bokyung Son , Sumin Park , Jungho Im , Seohui Park , Yinghai Ke , Lindi J. Quackenbush

Abstract In this research, a new drought monitoring approach with an adaptive index—Vector Projection Analysis (VPA) and Vector Projection Index of Drought (VPID)— was developed that considers multiple drought indicators in various climate zones across the Contiguous United States (CONUS) and East Asia. A major advantage of VPA is that it uses multiple dependent variables (i.e., surface-based drought indices) and multiple independent variables (i.e., satellite-derived drought factors) to capture varied climate and environmental characteristics. Therefore, the VPA-based indices can be adopted for different drought types (i.e., meteorological, agricultural and hydrological droughts) depending on the user's selection of drought factors and indices. In VPA, the weights of each drought factor are generated through correlations (r) between each dependent variable and a satellite-derived drought factor that is obtained from the new generation sensor systems, Visible Infrared Imaging Radiometer Suite (VIIRS) and Global Precipitation Mission (GPM). Three schemes of VPID with different combinations of variables focused on integrated-, short-, and long-term drought (VPIDinte, VPIDshort, and VPIDlong, respectively) were evaluated over the CONUS and East Asia. All three schemes showed good agreement with surface-based drought indices resulting in averaged r (from 0.42 to 0.68) across both study areas. While VPIDshort or VPIDlong were more correlated with short (1–3 months) or long (9–12 months) term surface-based drought indices, VPIDinte provided a more generalized (i.e., integrated from 1 to 12 months) index that is suitable for varied drought conditions. The spatial distributions of drought from VPIDs agreed with United States Drought Monitor (USDM) data and Emergency Events Database across East Asia for both extreme drought and normal conditions. In particular, the drought areas of VPIDinte corresponded with the USDM results with r = 0.83 and root mean square error = 9.4%. Crop yield trends were also consistent with the VPIDs results in most years for both study areas. The proposed approach, VPA and VPID, can be adopted for any region with different combinations of surface-based drought indices and satellite-derived drought factors.

中文翻译:

一种新的干旱监测方法:矢量投影分析 (VPA)

摘要 在这项研究中,开发了一种具有自适应指数的新干旱监测方法——矢量投影分析 (VPA) 和矢量干旱矢量投影指数 (VPID)——考虑了美国本土 (CONUS) 不同气候区的多个干旱指标。和东亚。VPA 的一个主要优点是它使用多个因变量(即基于地表的干旱指数)和多个自变量(即卫星衍生的干旱因子)来捕捉不同的气候和环境特征。因此,根据用户对干旱因子和指数的选择,可以针对不同的干旱类型(即气象、农业和水文干旱)采用基于 VPA 的指数。在 VPA 中,每个干旱因子的权重是通过每个因变量与从新一代传感器系统、可见红外成像辐射计套件 (VIIRS) 和全球降水任务 (GPM) 获得的卫星衍生干旱因子之间的相关性 (r) 生成的。在美国本土和东亚地区评估了三种不同变量组合的 VPID 方案,分别侧重于综合干旱、短期干旱和长期干旱(分别为 VPIDinte、VPIDshort 和 VPIDlong)。所有三个方案都显示出与地表干旱指数的良好一致性,导致两个研究区域的平均 r(从 0.42 到 0.68)。虽然 VPIDshort 或 VPIDlong 与短期(1-3 个月)或长期(9-12 个月)基于地表的干旱指数更相关,但 VPIDinte 提供了更广泛的(即,综合 1 至 12 个月)指数,适用于各种干旱条件。来自 VPID 的干旱空间分布与美国干旱监测 (USDM) 数据和东亚紧急事件数据库的极端干旱和正常条件一致。特别是,VPIDinte 的干旱区与 USDM 结果一致,r = 0.83,均方根误差 = 9.4%。在两个研究区域的大多数年份中,作物产量趋势也与 VPID 结果一致。所提出的方法,即 VPA 和 VPID,可用于任何具有不同地表干旱指数和卫星衍生干旱因子组合的地区。来自 VPID 的干旱空间分布与美国干旱监测 (USDM) 数据和东亚紧急事件数据库的极端干旱和正常条件一致。特别是,VPIDinte 的干旱区与 USDM 结果一致,r = 0.83,均方根误差 = 9.4%。在两个研究区域的大多数年份中,作物产量趋势也与 VPID 结果一致。所提出的方法,VPA 和 VPID,可用于任何具有不同地表干旱指数和卫星衍生干旱因子组合的地区。来自 VPID 的干旱空间分布与美国干旱监测 (USDM) 数据和东亚紧急事件数据库的极端干旱和正常条件一致。特别是,VPIDinte 的干旱区与 USDM 结果一致,r = 0.83,均方根误差 = 9.4%。在两个研究区域的大多数年份中,作物产量趋势也与 VPID 结果一致。所提出的方法,VPA 和 VPID,可用于任何具有不同地表干旱指数和卫星衍生干旱因子组合的地区。在两个研究区域的大多数年份中,作物产量趋势也与 VPID 结果一致。所提出的方法,VPA 和 VPID,可用于任何具有不同地表干旱指数和卫星衍生干旱因子组合的地区。在两个研究区域的大多数年份中,作物产量趋势也与 VPID 结果一致。所提出的方法,即 VPA 和 VPID,可用于任何具有不同地表干旱指数和卫星衍生干旱因子组合的地区。
更新日期:2021-01-01
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